Search Results

This study evaluated the effectiveness of alternative workload-based interventions intended to restore driver alertness following drowsy episodes. Unlike traditional drowsy driving studies, this experiment did not target sleep-deprived individuals, but rather studied normally rested drivers under the assumption that low-workload environments could trigger drowsy driving episodes. The study served as a proof of concept for varying the nature and onset of countermeasure interventions intended to disrupt the drowsiness cycle. Interventions to combat drowsiness attempted to target driver workload, either physical or cognitive, and included two primary treatment conditions: 1) physical workload to increase driver steering demands and 2) trivia-based interactive games to mentally challenge drivers. A benchmark comparison condition using music was also investigated to contrast the relative influence of workload-based interventions with passive listening to musical arrangements.

This study evaluated the performance of heavy vehicle crash avoidance systems (CASs) by collecting naturalistic driving data from 150 truck tractors equipped with Meritor WABCO OnGuardTM or Bendix® Wingman® AdvancedTM products. These CASs provide drivers with audio-visual alerts of potential conflicts, and can apply automatic braking to mitigate or prevent a potential collision. Each truck tractor participated for up to one year between 2013 and 2015. Videos of the forward roadway and drivers’ faces were collected along with vehicle network data while drivers performed their normal duties on revenue-producing routes. The study evaluated the performance of CAS activations by classifying them into three categories based on whether a valid object was being tracked and whether drivers needed to react immediately.

This study presents a long-term examination of the effects of two types of perceptual-cognitive brain training programs on senior driver behavior and on-road driving performance. Seniors (70+) engaged in either a Toyota-designed in-vehicle training program based on implicit learning principles or a commercially available computer-based training program developed by Posit Science. Another group served as a no-contact control group; total enrollment was 55 participants. Participants completed a series of four experimental sessions: (1) baseline pre-training, (2) immediate post-training, (3) 6-9 months post-training, and (4) 12-16 months post-training. Experimental metrics taken at each session included measures of vehicle control and driver glance behavior on public roads.

An analysis of the first 35 back-over crashes reported by NHTSA's Special Crash Investigations unit was undertaken with two objectives: (1) to test a hypothesized classification of backing crashes into types, and (2) to characterize scenario-specific conditions that may drive countermeasure development requirements and/or objective test development requirements. Backing crash cases were sorted by type, and then analyzed in terms of key features. Subsequent modeling of these SCI cases was done using an adaptation of the Driving Reliability and Error Analysis Methodology (DREAM) and Cognitive Reliability and Error Analysis Methodology (CREAM) (similar to previous applications, for instance, by Ljung and Sandin to lane departure crashes [10]), which is felt to provide a useful tool for crash avoidance technology development.

Forward Collision Warning (FCW) systems are intended to alert drivers when they may be at risk of a rear-end crash with a vehicle directly ahead unless they take immediate action. A forward collision visual alert (FCVA) is recommended as part of a multi-modality FCW system crash alert approach also including auditory and/or haptic crash alert components. SAE J2400 recommends that a conventional dashboard location shall not be used for the FCVA, since such an alert may distract the driver from the crash threat ahead (instead of helping the driver visually orient toward the crash threat). This research examined the merit of this recommendation by examining the effectiveness of instrument panel, head-up display, and (vehicle-centerline) top-of-dashboard FCVA candidates. In this static on-road study, 49 subjects (20–70 years old) made rapid judgments on the presence and nature of scene changes over two successive forward scene exposures controlled by a visual occlusion window.

Driver distraction, defined here as engaging in a secondary task or activity that is not central to the primary task of driving, has been shown to be a contributing factor for many crashes. The secondary tasks and other activities in which drivers choose to engage while driving is also known to be highly varied, including very complex activities(e.g., text messaging on a cellular device) to very simple activities (e.g., selecting a radio preset). Several important distinctions affect the relative risk of engaging in these tasks. Recent data from large-scale instrumented vehicle studies (i.e., “naturalistic” driving studies like the recently released “100 car study” (1)) have begun to provide data where the relative risk, in terms of crash and near crash involvement, can be directly assessed for differing secondary tasks. These data have provided some important insights into the features that create risk.

This paper discusses radio usage habits observed during analysis of 700 hours of video sampled from the 100-Car Naturalistic Driving Study database. Analysts used large-scale printouts of each vehicle's radio faceplate and recorded interactions based on video analysis of hand movement and location (without the assistance of audio recordings). The duration and specific manipulations or adjustments were recorded for each interaction. The results summarize the length and type of interactions, most often-used controls, and total percentage of time drivers interacted with the radio.

Twent-two participants of varying ages detected roadside targets in two consecutive dynamic evaluations of a horizontally swiveling headlamp vehicle and a vehicle with the same headlamps that did not swivel. Participants detected targets as they drove unlighted low-speed public roads. Scenarios encountered were intersection turns, and curves with approximate radii of 70-90m, 120-140m, 170-190m, and 215-220m. Results from the first study found improved detection distances from the swiveling headlamps in left curves, but unexpectedly decreased detection distances in larger radius right hand curves. The swiveling algorithm was altered for the second study, and the headlamps used did not have the same beam pattern as in the first study. Results from the second study again found improved detection distances from the swiveling headlamps while in the larger radius right hand curves fixed and swivel were not statistically different.

This paper describes an effort to gather lane-changing and passing driver behavior data using naturalistic observation methods. Participants were ordinary commuters who drove instrumented vehicles to and from work while data were automatically collected. The three types of data included driver, vehicle response, and vehicle interaction data via collected video, vehicle sensor, and radar systems. These data were combined using a data integration system to understand lane-change and passing maneuvers. Developed specifically for this project, this system allows analysts to understand and characterize lane-changing and passing maneuvers by presenting the three types of data in an intuitive, integrated manner. The integrated data will facilitate understanding of driver behavior. This understanding will assist designers in the development of future crash reduction countermeasures including Crash Avoidance Systems (CAS).